Energy control system employing automated validation, estimation, and editing rules

ABSTRACT

An energy control system includes a facility model processor, a post VEE readings data stores, a VEE configuration engine, and a global model module. The facility model processor employs interval based energy consumption streams corresponding to a facility to develop and maintain weather-normalized baseline energy consumption data for the facility. The post VEE readings data stores tagged energy consumption data sets that are each associated with a corresponding one of said interval based energy consumption streams, each of said tagged energy consumption data sets comprising groups of contiguous interval values tagged as having been validated, wherein said groups correspond to correct data. The VEE configuration engine reads the post VEE readings data stores upon initiation of an event and, for the each of the tagged energy consumption data sets, creates anomalies having different durations using only the groups of contiguous interval values, and generates estimates for the anomalies by employing estimation techniques and, for each of the different durations, selects one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the interval based energy consumption streams. The global model module receives the weather-normalized baseline energy consumption data and post VEE readings data, and develops an energy consumption model based on the weather-normalized baseline energy consumption data and the post VEE readings data, and controls overall energy consumption within the facility based on the energy consumption model by scheduling run times of one or more building elements.

This application is a continuation of the following U.S. PatentApplication, which is herein incorporated by reference in its entirety.

FILING Ser. No. DATE TITLE 15/280,606 Sep. 29, 2016 ENERGY BASELININGSYSTEM (ENER.0151) INCLUDING AUTOMATED VALIDATION, ESTIMATION, ANDEDITING RULES CONFIGURATION ENGINE

This application is related to the following co-pending U.S. PatentApplications, each of which has a common assignee and common inventors.

FILING Ser. No. DATE TITLE 15/280,526 Sep. 29, 2016 APPARATUS AND METHODFOR (ENER.0147) AUTOMATED VALIDATION, ESTIMATION, AND EDITINGCONFIGURATION 15/280,542 Sep. 29, 2016 AUTOMATED VALIDATION, (ENER.0148)ESTIMATION, AND EDITING PROCESSOR 15/280,569 Sep. 29, 2016 NETWORKOPERATIONS CENTER (ENER.0149) INCLUDING AUTOMATED VALIDATION,ESTIMATION, AND EDITING CONFIGURATION ENGINE 15/280,581 Sep. 29, 2016APPARATUS AND METHOD FOR (ENER.0150) AUTOMATED CONFIGURATION OFESTIMATION RULES IN A NETWORK OPERATIONS CENTER — — METHOD AND APPARATUSFOR (ENER.0151-C2) AUTOMATED BUILDING ENERGY CONTROL 15/280,622 Sep. 29,2016 DEMAND RESPONSE DISPATCH (ENER.0152) PREDICTION SYSTEM INCLUDINGAUTOMATED VALIDATION, ESTIMATION, AND EDITING RULES CONFIGURATION ENGINE15/280,646 Sep. 29, 2016 BROWN OUT PREDICTION (ENER.0153) SYSTEMINCLUDING AUTOMATED VALIDATION, ESTIMATION, AND EDITING RULESCONFIGURATION ENGINE 15/280,664 Sep. 29, 2016 BUILDING CONTROL SYSTEM(ENER.0154) INCLUDING AUTOMATED VALIDATION, ESTIMATION, AND EDITINGRULES CONFIGURATION ENGINE

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates in general to the field energy consumption, andmore particularly to an apparatus and method for automated metering datavalidation, estimation, and editing, and applications thereof.

Description of the Related Art

One problem with resources such as electricity, water, fossil fuels, andtheir derivatives (e.g., natural gas) is related to supply and demand.That is, production of a resource often is not in synchronization withdemand for the resource. In addition, the delivery and transportinfrastructure for these resources is limited in that it cannotinstantaneously match production levels to provide for constantlyfluctuating consumption levels. As anyone who has participated in arolling blackout will concur, the times are more and more frequent whenresource consumers are forced to face the realities of limited resourceproduction.

Another problem with resources such as water and fossil fuels (which areubiquitously employed to produce electricity) is their limited supplyalong with the detrimental impacts (e.g., carbon emissions) of theiruse. Public and political pressure for conservation of resources isprevalent, and the effects of this pressure is experienced across thespectrum of resource providers, resource producers and managers, andresource consumers.

It is no surprise, then, that the electrical power generation anddistribution community has been taking proactive measures to protectlimited instantaneous supplies of electrical power by 1) imposing demandcharges on consumers in addition to their monthly usage charge and 2)providing incentives for conservation in the form of rebates and reducedcharges. In prior years, consumers merely paid for the total amount ofpower that they consumed over a billing period. Today, most energysuppliers are not only charging customers for the total amount ofelectricity they have consumed over the billing period, but they areadditionally imposing time of use charges and charging for peak demand.Peak demand is the greatest amount of energy that a customer uses duringa measured period of time, typically on the order of minutes. Time ofuse charges fluctuate throughout the day to dissuade customers fromusing energy during peak consumption hours. Moreover, energy suppliersare providing rebate and incentive programs that reward consumers for socalled energy efficiency upgrades (e.g., lighting and surroundingenvironment intelligently controlled, efficient cooling andrefrigeration, etc.) in their facilities that result in reductions ofboth peak consumption, time of use consumption shifting, and overallenergy consumption. Similar programs are prevalent in the waterproduction and consumption community as well. It is anticipated thatsuch programs will extend to other limited supply energy sources, suchas, but not limited to, natural gas, nuclear energy, and other fuelsources.

Demand reduction and energy efficiency programs may be implemented andadministered directly by energy providers (i.e., the utilitiesthemselves) or they may be contracted out to third parties, so calledenergy services companies (ESCOs). ESCOs directly contract with energyconsumers and also contract with the energy providers to, say, reducethe demand of a certain resource in a certain area by a specifiedpercentage, where the reduction may be constrained to a certain periodof time (i.e., via a demand response program). Or, the reduction effortmay be ongoing (i.e., via an energy efficiency program).

The above scenarios are merely examples of the types of programs thatare employed in the art to reduce consumption and foster conservation oflimited resources. Regardless of the vehicle that is employed, what isimportant to both producers and consumers is that they be able tounderstand and appreciate the effects of demand reduction and efficiencyimprovements that are performed. Sometimes the understanding andappreciation can occur hours, days, or even weeks after the consumptionoccurs. But the present inventors have observed that there is increasingdesire in the art for such understanding and appreciation to occurminutes after the consumption occurs, such as in real time and near realtime systems. This disclosure is provided to solve problems that arepresent in real time and near real time systems.

As one skilled in the art will appreciate, many real time and near realtime systems perform operations based on energy consumption dataprovided by streaming energy consumption metering sources such as smartmeters and building automation system meters that measure the energyconsumed by one or more corresponding devices and periodically transmitmeasurements over a conventional wired or wireless network. One skilledwill also appreciate that the measurements that are transported over thenetworks may interspersed with intermittent errors due to power outages,device or metering source malfunctions, weather conditions affectingtransmission, network problems, etc.

In fact, streaming data quality and accuracy problems are so common thatstandardized processes are being developed that set requirements forvalidation, estimation, and editing (VEE) of metered energy consumptiondata. These standards address such things as power outages, missinginterval values, atypical interval values, suspect groups of intervalvalues, time stamping, reactive energy issues, and a significant numberof estimation methods to be employed for particular types of detectedanomalies, specific types of meters, and certain known scenarios. Thus,received data must be first validated, that is, examined and taggedeither as valid data or anomalous data. Next, estimation techniques areemployed to generate estimates for the anomalous data. Finally, editingoccurs where the anomalous data is replaced with the estimates,resulting is what is known as post VEE data. The post VEE data is thenemployed by components within the real time and near real time systemsto perform their respective operations and control functions.

Because these systems perform their operations and functions in realtime or near real time, the processes that perform VEE are timeconstrained to provide post VEE data. And these systems attempt tobalance processing capacity (and ultimately, cost) with post VEE dataaccuracy, typically as a function of number of streaming sourcesprocessed and the time allocated for VEE processing.

The present inventors have noted that virtually all real time and nearreal time systems that process a substantial number of energyconsumption streams (say, between 50 and 50,000 streams) employso-called “lightweight” VEE in order to achieve required throughput. Asone skilled will appreciate, many different techniques exist to detectand correct anomalous data, where more accurate post VEE data can be hadby using techniques that are well suited for individual stream types andanomaly durations. But to tailor VEE techniques for each stream in asystem that processes a substantial number of streams would require aninordinate amount of data analyst time and effort. Consequently,lightweight VEE is a compromise, typically employing a small number ofVEE techniques for all streams processed by a system. Throughput isachieved, data analyst time is controlled, and post VEE data is acceptedas being as accurate as can be had.

The present inventors have also noted, though, that there is increasingdesire in the art for more accurate post VEE data for use by real timeand near real time systems, over that which is presently available.Accordingly, what is needed is an apparatus and method for automaticallyconfiguring VEE techniques in real time and near real time systems.

What is also needed is an automated validation, estimation, and editingprocessor that configures and dynamically optimizes VEE technique forindividual data streams.

What is additionally needed is a network operations center thatautomatically configures and dynamically optimizes validation,estimation, and editing techniques corresponding to a plurality ofenergy consumption data streams.

What is furthermore needed is an energy baselining system thatautomatically configures and dynamically optimizes validation,estimation, and editing techniques corresponding to a plurality ofenergy consumption data streams.

What is moreover needed is a demand response prediction system thatautomatically configures and dynamically optimizes validation,estimation, and editing techniques corresponding to a plurality ofenergy consumption data streams.

What is furthermore needed is a brown out prediction system thatautomatically configures and dynamically optimizes validation,estimation, and editing techniques corresponding to a plurality ofenergy consumption data streams.

What is yet additionally needed is a building control system thatautomatically configures and dynamically optimizes validation,estimation, and editing techniques corresponding to a plurality ofenergy consumption data streams.

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solvingthe above-noted problems and addresses other problems, disadvantages,and limitations of the prior art. The present invention provides asuperior technique for automatically and configuring and dynamicallyoptimizing validation, estimation, and editing techniques correspondingto a plurality of energy consumption data streams that are processed byreal time and near real time systems.

One aspect of the present invention contemplates an energy controlsystem. The system includes a facility model processor, a postvalidation, estimating, and editing (VEE) readings data stores, a VEEconfiguration engine, and a global model module. The facility modelprocessor receives one or more interval based energy consumption streamscorresponding to a facility, and develops and maintainsweather-normalized baseline energy consumption data for the facility,where the weather-normalized baseline energy consumption data is derivedfrom training data for the facility. The post VEE readings data storesprovides tagged energy consumption data sets that are each associatedwith a corresponding one of the interval based energy consumptionstreams, each of the tagged energy consumption data sets comprisinggroups of contiguous interval values tagged as having been validated,where the groups correspond to correct data. The VEE configurationengine reads the post VEE readings data stores upon initiation of anevent and, for the each of the tagged energy consumption data sets,creates anomalies having different durations using only the groups ofcontiguous interval values, and generates estimates for the anomalies byemploying estimation techniques for each of the different durations and,for the each of the different durations, selects a corresponding one ofthe estimation techniques for subsequent employment when performing VEEof subsequent energy consumption data associated with the each of thedifferent durations for the corresponding one of the interval basedenergy consumption streams. The global model module receives theweather-normalized baseline energy consumption data and post VEEreadings data, and develops an energy consumption model based on theweather-normalized baseline energy consumption data and the post VEEreadings data, and controls overall energy consumption within thefacility based on the energy consumption model by scheduling run timesof one or more building elements.

Another aspect of the present invention envisages an energy controlsystem. The system includes a facility model processor, a sourcesmetadata stores, a post validation, estimation, and editing (VEE)readings data stores, a VEE configuration engine, and a global modelmodule. The facility model processor receives one or more interval basedenergy consumption streams corresponding to a facility, and develops andmaintains weather-normalized baseline energy consumption data for thefacility, where the weather-normalized baseline energy consumption datais derived from training data for the facility. The sources metadatastores provides VEE rules corresponding to each of the interval basedenergy consumption streams for performing VEE. The post VEE readingsdata stores provides tagged energy consumption data sets that are eachassociated with a corresponding one of the interval based energyconsumption streams, each of the tagged energy consumption data setscomprising groups of contiguous interval values tagged as having beenvalidated, where the groups correspond to correct data. The VEEconfiguration engine reads the post VEE readings data stores uponinitiation of an event and, for the each of the tagged energyconsumption data sets, creates anomalies having different durationsusing only the groups of contiguous interval values, and generatesestimates for the anomalies by employing estimation techniques for eachof the different durations and, for the each of the different durations,selects a corresponding one of the estimation techniques for subsequentemployment when performing VEE of subsequent energy consumption dataassociated with the each of the different durations for thecorresponding one of the interval based energy consumption streams. Theglobal model module receives the weather-normalized baseline energyconsumption data and post VEE readings data, and develops an energyconsumption model based on the weather-normalized baseline energyconsumption data and the post VEE readings data, and controls overallenergy consumption within the facility based on the energy consumptionmodel by scheduling run times of one or more building elements.

A further aspect of the present invention contemplates a method forcontrolling energy consumption. The method includes employing intervalbased energy consumption streams corresponding to a facility to developand maintain weather-normalized baseline energy consumption data for thefacility, where the weather-normalized baseline energy consumption datais derived from training data for the facility; providing tagged energyconsumption data sets that are each associated with a corresponding oneof the interval based energy consumption streams, each of the taggedenergy consumption data sets comprising groups of contiguous intervalvalues tagged as having been validated, wherein said groups correspondto correct data; reading the post VEE readings data stores uponinitiation of an event; for the each of the tagged energy consumptiondata sets, creating anomalies having different durations using only thegroups of contiguous interval values; generating estimates for theanomalies by employing estimation techniques for each of the differentdurations; for the each of the different durations, selecting acorresponding one of the estimation techniques for subsequent employmentwhen performing VEE of subsequent energy consumption data associatedwith the each of the different durations for the corresponding one ofthe interval based energy consumption streams; and receiving theweather-normalized baseline energy consumption data and post VEEreadings data, and developing an energy consumption model based on theweather-normalized baseline energy consumption data and the post VEEreadings data, and controlling overall energy consumption within thefacility based on the energy consumption model by scheduling run timesof one or more building elements.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the presentinvention will become better understood with regard to the followingdescription, and accompanying drawings where:

FIG. 1 is a timing diagram illustrating a present day energy consumptionstream, such as may be provided by an automatic meter reading (AMR)meter;

FIG. 2 is a block diagram depicting a present day network operationscenter (NOC) that employs conventional validation, estimation, andediting (VEE) mechanisms to detect and correct anomalies in a pluralityof energy consumption streams;

FIG. 3 is a block diagram featuring a network operations center (NOC)according to the present invention that includes an automated VEE rulesconfiguration engine that enables a VEE processor to detect and correctanomalies in a plurality of energy consumption streams;

FIG. 4 is a block diagram showing how a VEE configuration engineaccording to the present invention interfaces to a VEE processor;

FIG. 5 is a flow diagram illustrating a method according to the presentinvention for automatically configuring detection rules for use by a VEEprocessor;

FIG. 6 is a flow diagram detailing a method according to the presentinvention for automatically configuring estimation rules for use by aVEE processor;

FIG. 7 is a block diagram illustrating an energy baselining systemaccording to the present invention that includes an automated VEE rulesconfiguration engine;

FIG. 8 is a block diagram a demand response dispatch prediction systemaccording to the present invention that includes an automated VEE rulesconfiguration engine;

FIG. 9 is a block diagram featuring a brown out prediction systemaccording to the present invention that includes an automated VEE rulesconfiguration engine; and

FIG. 10 is a block diagram showing an automated building control systemaccording to the present invention that includes an automated VEE rulesconfiguration engine.

DETAILED DESCRIPTION

Exemplary and illustrative embodiments of the invention are describedbelow. In the interest of clarity, not all features of an actualimplementation are described in this specification, for those skilled inthe art will appreciate that in the development of any such actualembodiment, numerous implementation specific decisions are made toachieve specific goals, such as compliance with system-related andbusiness related constraints, which vary from one implementation toanother. Furthermore, it will be appreciated that such a developmenteffort might be complex and time-consuming, but would nevertheless be aroutine undertaking for those of ordinary skill in the art having thebenefit of this disclosure. Various modifications to the preferredembodiment will be apparent to those skilled in the art, and the generalprinciples defined herein may be applied to other embodiments.Therefore, the present invention is not intended to be limited to theparticular embodiments shown and described herein, but is to be accordedthe widest scope consistent with the principles and novel featuresherein disclosed.

The present invention will now be described with reference to theattached figures. Various structures, systems, and devices areschematically depicted in the drawings for purposes of explanation onlyand so as to not obscure the present invention with details that arewell known to those skilled in the art. Nevertheless, the attacheddrawings are included to describe and explain illustrative examples ofthe present invention. The words and phrases used herein should beunderstood and interpreted to have a meaning consistent with theunderstanding of those words and phrases by those skilled in therelevant art. No special definition of a term or phrase (i.e., adefinition that is different from the ordinary and customary meaning asunderstood by those skilled in the art) is intended to be implied byconsistent usage of the term or phrase herein. To the extent that a termor phrase is intended to have a special meaning (i.e., a meaning otherthan that understood by skilled artisans) such a special definition willbe expressly set forth in the specification in a definitional mannerthat directly and unequivocally provides the special definition for theterm or phrase.

In view of the above background discussion on metering data validation,estimation and editing (VEE) and associated techniques employed withinpresent day systems to perform VEE for purposes of real time and nearreal time control, a discussion of the limitations and disadvantages ofpresent day VEE configuration and processing mechanisms will bepresented with reference to FIGS. 1-2. Following this, a discussion ofthe present invention will be provided with reference to FIGS. 3-10. Thepresent invention overcomes the above noted limitations anddisadvantages, and others, by providing apparatus and methods forautomated VEE rules configuration which exhibit significantly increasedaccuracies and reduced manpower requirements over that which hasheretofore been provided.

Turning to FIG. 1, a timing diagram is presented illustrating a presentday energy consumption stream 100, such as may be provided by anautomatic meter reading (AMR) meter or similar device that measures,registers, and transmits energy consumption values. As one skilled inthe art will appreciate, the stream 100 is representative of energyconsumed by a metered entity such as, but not limited to, a house, anapartment, a building, groups of buildings, a substation, arefrigeration unit, an industrial machine, an electrical grid subset,etc. Automated meter reading (AMR) mechanisms abound in the art. Thesemechanisms register energy consumption and generate and transmitperiodic energy consumption data, (i.e., the stream 100), such as isshown in timing diagram. The precise value of the periodic time intervalis determined according to specific end use and may typically vary from24 hours (e.g., a daily consumption value) down to one minute. Thestream 100 may be received by entities such as, but not limited to, gridoperators, energy service companies (ESCOs), building automationservices providers, and network operations centers (NOCs) that employthe stream 100 in conjunction with hundreds to thousands of otherstreams for purposes of performing control functions (e.g., monthlybilling, near real time presentation of energy use versus predictedenergy use, occupancy management, energy purchases and trades, gridmanagement, multi-facility building automation, demand response programmanagement, etc.).

Regardless of its end use, it is well known in the art that any numberof factors can affect the quality and accuracy of one or more groups ofcontiguous data points in the stream 100 as the stream 100 is generated,transmitted, and received by a receiving entity. For instance, there maybe problems with the metering mechanism itself that result in periodicor aperiodic erroneous data points. There may be intermittent failureswithin transmission or relay devices that result in in periodic oraperiodic erroneous data points. There may be failures in receptionequipment at the destination that result in in periodic or aperiodicerroneous data points. Intermittent power outages due to weather orother causes may occur. The foregoing is not an exhaustive list offactors affecting the quality and accuracy of data that is transmittedin the stream 100, but such factors are sufficient to illustrate thatinaccuracies in streaming data are characteristic of this field of art.

Streaming data quality and accuracy problems prevail, so much so thatoperators, industry groups, regions, and even countries are developingtheir own standardized processes that set requirements for validation,estimation, and editing of metered energy consumption data. Thesestandards address such things as power outages, missing interval values,atypical interval values, suspect groups of interval values, timestamping, reactive energy issues, and a significant number of estimationmethods to be employed for particular types of detected anomalies,specific types of meters, and certain known scenarios.

The energy consumption data stream 100 shown in the timing diagramexhibits two representative anomalies 101-102, where an anomaly isdefined as one or more contiguous interval values (i.e., adjacent pointsin the stream 100) that appear to be erroneous or inaccurate. A firstrepresentative anomaly 101 may be due to an abnormally high reading, anabnormally high rate of reading change, or both. A second representativeanomaly 102 may be due to a power outage, a meter reading gap (i.e.,intermittent meter failure), a zero reading, or a combination of thesecauses.

As the data stream 100 is received, it is incumbent on an end user toperform VEE in a manner and time frame that is consistent with end useapplication. For example, if the end use application is monthly billingfor energy use, then VEE protocols are applied to the data points in thestream 100 in order to yield highly accurate post-VEE readings. Andsince billing is not a time critical application, constraints on thecomputer processing devices required to process the data points in thestream 100 are quite relaxed. At the other extreme are so called realtime and near real time end use applications that require accuratepost-VEE readings, but which are required to balance accuracyrequirements with processing constraints as a function of data streamprocessing count, computer processing device availability, and servicelevel agreements (SLAs). An SLA, among other parameters, specifies amaximum time for a given computer process to process its input data suchthat processed data (i.e., output) is provided to a consumer process. Inthe case of VEE, SLAs according to application will specify maximumtimes allocated for performing VEE on a received data stream 100 toproduce post-VEE data, i.e., data that has been verified, estimated, andedited according to prescribed VEE protocols. The present applicationfocuses on the latter group of end use applications, namely, real timeand near real time applications.

Referring now to FIG. 2 a block diagram 200 is presented depicting apresent day network operations center (NOC) 210 that employsconventional validation, estimation, and editing (VEE) mechanisms todetect and correct anomalies in a plurality of energy consumptionstreams 204. The diagram 200 shows representative metering devices201-203 that generate and transmit the streams 204 such as one or moreAMR meters 201, one or more building automation system (BAS) meteringdevices 202, and one or more other metering devices 203. The othermetering devices 203 may directly measure, generate, and transmitstreams 204, or they may relay the streams 204, as in the case of a gridoperator relaying streaming data to an ESCO. The streams 204 may betransmitted to the NOC 210 by any well-known streaming mechanism suchas, but not limited to, wired or wireless networks, radio frequencynetworks, cellular networks, satellite communications, etc.

The NOC 210 comprises a receiver 211 that is coupled to each of thestreams 204 and that performs the functions required to translatesignals corresponding to each of the streams 204 into data that issuitable for VEE processing within the NOC 210. Accordingly, thereceiver 211 is coupled to a VEE processor 212 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 204. RCV.1-RCV.N are coupled to the VEEprocessor 212. The VEE processor 212 is coupled to one or more processcontrol elements 213 via busses PC.1-PC.N. A service level agreement(SLA) may be prescribed for processing of the streams 204 by the VEEprocessor 212, and may establish a maximum processing time from receiptof the streams 204 by the VEE processor 212 until post-VEE readings areprovided to the process control elements 213. The process controlelements 213 may be coupled to corresponding system elements 221, whichmay be internal or external to the NOC 210. In the diagram 200, thesystem elements 221 are depicted external to the NOC 210. A post-VEEreadings stores 216 is coupled to the VEE processor 212 via bus PV. Asources metadata stores 214 may be coupled to the VEE processor 212 viabus RULES. One or more data analyst workstations 215 may be coupled tothe sources metadata stores 214.

In operation, a typical NOC 210 may receive tens of thousands of energyconsumption data streams 204, and may be constrained by SLA to performVEE functions on the order of minutes for real time and near real timeapplications. For purposes of the present application, the presentinventors have observed that SLAs of approximately five minutes aretypical for such applications, and such maximum processing times toperform VEE functions will be employed hereinafter for purposes ofteaching the present invention. Accordingly, the streams 204 arereceived at the NOC 210 by the receiver 211, which translates signals inthe streams 204 into data suitable for execution of VEE functions by theVEE processor 212. As noted above, interval times for the streams 204may range from approximately one minute up to approximately 24 hours.

As a next interval value is translated for a given stream 204, the VEEprocessor 212 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to one or more of the process controlelements 213 over buses PC.1-PC.N, as required by application. The nextinterval value is also provided to the post-VEE readings stores 216 overbus PV, where it is available for other processing functions, ifrequired. If the next interval value is deemed an anomaly, then the VEEprocessor 212 may employ estimation and editing rules for the givenstream 204 to replace (i.e., “edit”) the next interval value with anestimated next interval value. The estimated next interval value may beprovided to one or more of the process control elements 213 over busesPC.1-PC.N, as required by application. The estimated next interval valueis also provided to the post-VEE readings stores 216 over bus PV, whereit is available for other processing functions, if required. Each of thedata streams 204 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 214 tothe VEE processor 212 over bus RULES.

As required by application, post-VEE readings corresponding to one ormore of the streams 204 may be provided according to service levelagreement to one or more of the process control elements 213. Theprocess control elements 213 may then execute functions on the streamdata translated by the receiver 211 and VEE processor 212. According toresults of the executed functions, the process control elements 213 maydirect one or more of the system elements 221 to change state. Forinstance, in an energy efficiency real-time feed where one or more ofthe system elements 221 comprises a controllable video display, post-VEEdata may be translated by the one or more processing elements 213 intoreal-time electrical usage for a corresponding building along with aweather normalized usage baseline recommendation that allows a buildingowner to take action regarding current energy usage.

In a present day configuration, when a new stream 204 is added, one ormore data analysts 215 are required to generate a set of detection,estimation, and editing rules in the sources metadata stores 214 thatcorresponds to the new stream 204. Detection rules may comprise, but arenot limited to, gap detection, zero value detection, negative readingdetection, high-read (i.e., over threshold) detection, thresholdsetting, etc. Estimation rules are more complex, and may comprise, butare not limited to, average of N preceding and M subsequent intervalvalues, replacement with values from previous day of the week, linearinterpolation employing N preceding and M subsequent interval values,replace with data from same time previous week, replace with data fromsame time last week plus straight line interpolated constant, replacewith data from same time previous day plus straight line interpolatedconstant, replace with average binned data from previous month (e.g.,average of all 10:05 Monday interval values), replace with median binneddata from previous month (e.g., median of all 10:05 Monday intervalvalues), etc.

The present inventors have observed as well that, because of stringentVEE max processing time constraints, that analysts 215 or entities inauthority over the particular application of the NOC 210 (e.g., a gridoperator or regional transmission organization) select very simpledetection, estimation, and editing rules for most, if not all, datastreams 204 that are processed by the NOC 210. Such an approach forperforming VEE is taken in order to meet the VEE max processing timewhile at the same time minimizing very costly labor associated withanalyzing individual data streams 204 in order to determine VEE rulesthat result in more accurate post-VEE data.

In addition, the present inventors have noted that present day systemsfor performing VEE employ a one-size-fits-all approach to VEE rules,regardless of the duration of each anomaly, which is also referred to asa “gap.” For example, as one skilled in the art will appreciate, a gapof one interval, say five minutes, may be more accurately estimated bymerely employing a previous interval value (i.e., a first estimationrule), whereas a gap of 14 intervals, say 70 minutes, may be moreaccurately estimated by replacing the anomalous data with data from sametime last week plus straight line interpolated constant (i.e., a secondestimation rule). And a gap of 287 intervals, say 23 hours and 55minutes, may be more accurately estimated by replacing the anomalousdata with average binned data from previous month (i.e., a thirdestimation rule). However, present day VEE systems are not generallyconfigured to 1) group gaps according to duration, or 2) applyestimation rules selected from a plurality of estimation rules accordingto gap size in order to increase the accuracy of post-VEE readings. Thisis because, in real time and near real time systems, VEE processingconstraints and stream counts (tens of thousands) are prohibitive, thusprecluding any reasonable form of data analysis. For non-real timeapplications, a significant number of data analysts 215 may be employedto configured detection and estimation rules in order to increase theaccuracy of post-VEE readings. However, real time applications precludethe use of an army of data analysts 215.

Accordingly, the present inventors have noted that present day VEEsystems are limited in that they are labor intensive, since VEE rulesfor each added data stream 204 must be configured by a data analyst 215,and they are disadvantageous because their estimated post-VEE readingsare less accurate than that which is desired, as a result of applyingone-size-fits-all estimation rules for all types of streams and for allgap sizes.

The present invention overcomes the above noted limitations anddisadvantages, and others, by providing a technique for automatic VEErules configuration and dynamic VEE rules adjustment, which 1)eliminates labor (i.e., data analyst 215) requirements for configuringVEE rules and 2) employs estimation algorithms that are optimumaccording to stream type and gap size, thus resulting in remarkableincreases in post-VEE readings accuracy for real time and near real timeapplications. The present invention will now be discussed with referenceto FIGS. 3-10.

Turning now to FIG. 3, a block diagram 300 is presented featuring anetwork operations center (NOC) 310 according to the present inventionthat includes an automated VEE rules configuration engine 331 thatenables a VEE processor 312 to detect and correct anomalies in aplurality of energy consumption streams 304 much more accurately thanthat which has heretofore been provided.

The diagram 300 shows representative metering devices 301-303 thatgenerate and transmit the streams 304 such as one or more AMR meters301, one or more building automation system (BAS) metering devices 302,and one or more other metering devices 303. The other metering devices303 may directly measure, generate, and transmit streams 304, or theymay relay the streams 304, as in the case of a grid operator relayingstreaming data to an ESCO. The streams 304 may be transmitted to the NOC310 by any well-known streaming mechanism such as, but not limited to,wired or wireless networks, radio frequency networks, cellular networks,satellite communications, etc.

The NOC 310 comprises a receiver 311 that is coupled to each of thestreams 304 and that performs the functions required to translatesignals corresponding to each of the streams 304 into data that issuitable for VEE processing within the NOC 310. Accordingly, thereceiver 311 is coupled to a VEE processor 312 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 304. RCV.1-RCV.N are coupled to the VEEprocessor 312. The VEE processor 312 is coupled to one or more processcontrol elements 313 via busses PC.1-PC.N. A service level agreement(SLA) may be prescribed for processing of the streams 304 by the VEEprocessor 312, and may establish a maximum processing time from receiptof the streams 304 by the VEE processor 312 until post-VEE readings areprovided to the process control elements 313. The process controlelements 313 may be coupled to corresponding system elements 321, whichmay be internal or external to the NOC 310. In the diagram 300, thesystem elements 321 are depicted external to the NOC 310. A post-VEEreadings stores 316 is coupled to the VEE processor 312 via bus PV. Asources metadata stores 314 may be coupled to the VEE processor 312 viabus RULES. A VEE configuration engine 331 may be coupled to both thepost-VEE readings stores 316 and the sources metadata stores 314. Incontrast to the NOC 210 of FIG. 2, the present invention does notrequire a data analyst workstation 215 nor any number of data analystsproviding input thereto.

In operation, the NOC 310 may receive tens of thousands of energyconsumption data streams 304, and may be constrained by SLA to performVEE functions on the order of minutes for real time and near real timeapplications. In one embodiment SLAs of approximately five minutes arecontemplated, though other SLA processing times may be achievedaccording to the scope of the present disclosure. Accordingly, thestreams 304 are received at the NOC 310 by the receiver 311, whichtranslates signals in the streams 304 into data suitable for executionof VEE functions by the VEE processor 312. In one embodiment, intervaltimes for the streams 304 may range from approximately one minute up toapproximately 24 hours.

As a next interval value is translated for a given stream 304, the VEEprocessor 312 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to one or more of the process controlelements 313 over buses PC.1-PC.N, as required by application. The nextinterval value is also provided to the post-VEE readings stores 316 overbus PV, where it is available for other processing functions, ifrequired, and may also be accessed by the VEE configuration engine 331.If the next interval value is deemed an anomaly, then the VEE processor312 may employ estimation and editing rules for the given stream 304 toreplace (i.e., “edit”) the next interval value with an estimated nextinterval value. The estimated next interval value may be provided to oneor more of the process control elements 313 over buses PC.1-PC.N, asrequired by application. The estimated next interval value is alsoprovided to the post-VEE readings stores 316 over bus PV, where it isavailable for other processing functions, if required. Each of the datastreams 304 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 314 tothe VEE processor 312 over bus RULES.

As required by application, post-VEE readings corresponding to one ormore of the streams 304 may be provided according to service levelagreement to one or more of the process control elements 313. Theprocess control elements 313 may then execute functions on the streamdata translated by the receiver 311 and VEE processor 312. According toresults of the executed functions, the process control elements 313 maydirect one or more of the system elements 321 to change state. Forinstance, in an building automation system embodiment, where one or moreof the system elements 321 comprises a controllable building comfortmanagement system, post-VEE data may be translated by the one or moreprocessing elements 313 into control signals to direct heating,ventilation, and air-conditioning (HVAC) subsystems in a correspondingbuilding along to cycle, to decelerate schedules, to accelerateschedules, and etc., as a function of the post-VEE data, thus enablingthe building automation system to adjust comfort in accordance withpower consumed in real time.

In contrast to a present day configuration, the present inventionprovides for automatic configuration and dynamic optimization of VEErules for all streams 304 that are processed by the VEE processor 312.Accordingly, when a new stream 304 is added, the VEE configurationengine 331 generate a default set of detection, estimation, and editingrules in the sources metadata stores 314 that corresponds to the newstream 304. Detection rules may comprise, but are not limited to, gapdetection, zero value detection, negative reading detection, high-read(i.e., over threshold) detection, threshold setting, etc. Estimationrules are more complex, and may comprise, but are not limited to,average of N preceding and M subsequent interval values, replacementwith values from previous day of the week, linear interpolationemploying N preceding and M subsequent interval values, replace withdata from same time previous week, replace with data from same time lastweek plus straight line interpolated constant, replace with data fromsame time previous day plus straight line interpolated constant, replacewith average binned data from previous month (e.g., average of all 10:05Monday interval values), replace with median binned data from previousmonth (e.g., median of all 10:05 Monday interval values), etc.

The VEE configuration engine 331 thereafter periodically accesses thepost VEE readings stores 314 to analyze post VEE readings for the newstream 304 and for all other streams 304 processed by the NOC 310 to 1)adjust detection rules, 2) to group gaps in each stream 304 according togap size (i.e. duration of contiguous anomalous intervals in each stream304), and 3) to assign optimum estimation and editing rules for each gapsize group in each of the streams 304. In one embodiment, the VEEconfiguration engine 331 performs these noted functions serially on onestream 304 at a time, until all streams 304 have been analyzed and theircorresponding VEE rules have been updated. In one embodiment, the VEEconfiguration engine 331 performs its analysis and rules optimizationfunctions continuously. In another embodiment, the VEE configurationengine 331 performs its analysis and rules optimization functions as afunction of system processing load, where processing loads less than athreshold value enables the VEE configuration engine 331 to execute itsfunctions, and processing loads greater than the threshold precludesexecution of its functions. In one embodiment, the threshold is 50percent. In a further embodiment, the VEE configuration engine 331cycles serially through all the streams 304 every 24 hours. In yet afurther embodiment, the VEE configuration engine 331 cycles seriallythrough all the streams 304 every seven days. In yet another embodiment,the VEE configuration engine 331 cycles serially through all the streamsonce a month.

Advantageously, the NOC 310 according to the present invention requireslittle, if any, data analyst support for establishing and optimizing VEErules for individual streams. In addition, the VEE configuration engine331 according to the present invention dynamically optimizes VEE rulesfor each different gap size in individual streams 304, as will bedescribed in further detail below, by analyzing post VEE readings forthe streams 304, thus minimizing labor costs and maximizing accuracy ofpost VEE readings. Detection rules for a given stream 304 may beoptimized by analyzing post-VEE readings corresponding to other streams304 that are of similar device type, similar transmission medium, andsimilar interval values. Accordingly, values of detection rules such asrate of reading change, high read, dropped reading, etc., aredynamically adjusted for the given stream 304. Estimation and editingrules for a given stream 304 may be optimized by employing non-editedpost VEE readings for the given stream 304 to create a substantialnumber of anomalies for all gap sizes, executing all estimation andediting rules for each of the created anomalies, and selecting anestimation and editing rule for each gap size that minimizes errorbetween estimated and edited interval data points and true post VEEreadings from which the anomalies were created. In one embodiment, thenumber of created anomalies per stream 304 is greater than 10,000, andan estimation and editing rule for each gap size is selected thatminimizes root mean squared (RMS) error between estimated and editedinterval data points and true post VEE readings. In one embodiment, theVEE configuration engine 331 may employ up to 25 types of detectionrules along with associated threshold values such as, but not limitedto, the detection rules alluded to above. In one embodiment, the VEEconfiguration engine 331 may employ up to 25 types of estimation andediting rules such as, but not limited to, the estimation and editingrules alluded to above.

After a given stream 304 has been processed, the VEE configurationengine 331 updates VEE rules for the stream 304 in the sources metadatastores to replace previous VEE rules with updated, optimized VEE rules.A VEE rules record for the given stream 304 in the sources metadatastores 314 may comprise the following fields: STREAMID, STREAM TYPE,TRANSPORT TYPE, DETECTION RULE 1 (OPTIONAL THRESHOLD 1), . . . DETECTIONRULE 25 (OPTIONAL THRESHOLD 25), GAP SIZE 1, ESTIMATION/EDIT RULE 1, . .. GAP SIZE N, ESTIMATION/EDIT RULE N; where N equals the number of gapsizes for the given stream 304. In one embodiment, gap sizes for allstreams 304 are: 0-15 minutes, 15-60 minutes, 1-4 hours, 4-24 hours, 1-4days, and greater than four days. Thus, one of up to 25 estimation andediting rules is selected for each of the six above gap sizes, where theselect estimation and editing rule maximizes accuracy of post VEEreadings for its corresponding anomaly gap size.

Now referring to FIG. 4, a block diagram 400 is presented showing how aVEE configuration engine 420 according to the present inventioninterfaces to a VEE processor 410. The VEE processor 410 may select oneof N received streams RCV.1-RCV.N. A selected stream RCV.X is providedto a detect processor 411. The detect processor 411 is coupled to agroup processor 412 via an anomaly bus A.X. The group processor 412 iscoupled to an estimation/edit processor 413 via a grouped anomaly busG.X. The estimation/edit processor 413 generates an estimated and editedstream on bus ESTR.X, which is coupled to a post VEE readings stores416, and which may be provided to one or more process control busesPC.1-PC.N within a NOC. The estimation/edit processor 413 may alsoaccess the post VEE readings stores 416 to obtain post VEE readings forselected stream RCV.X at one or more previous times to estimate readingscorresponding to detected anomalies.

The VEE processor 410 may access a sources metadata stores 414 toprovide optimized detection rules to the detect processor 411 via busDRULES, group sizing rules to the group processor 411 via bus GRULES,and optimized estimation/editing rules to the estimation/edit processor413 via bus ERULES.

The VEE configuration engine 420 comprises a rules processor 421 that iscoupled to a timer 422 and a processing load monitor 423. The VEEconfiguration engine 420 is coupled to both the sources metadata stores414 and the post VEE readings stores 416.

In operation, as discussed above with reference to FIG. 3, the VEEprocessor 410 functions to VEE operations on energy consumption datacorresponding to a plurality of streams RCV.1-RCV.N to generate post VEEreadings for storage in the post

VEE readings stores 416, and which may be provided to one or moreprocess control elements via buses PC.1-PC.N for purposes according toapplication for the NOC. As stream data is available, the VEE processor410 must perform these VEE operations in accordance with service licenseagreement in order to present corrected and timely post VEE readings tothe one or more process control elements. In one embodiment, a pluralityof VEE processors 410 are disposed within the NOC to allow forsimultaneous VEE processing of a corresponding plurality of data streamsin order to meet throughput requirements according to number of datastreams and interval timing within each of the data streams.Accordingly, the detect processor 411 detects anomalies in the streamdata according to detection rules provided over bus DRULES. A detectedanomaly is declared to the group processor over A.X, which includesinterval time for the stream and number of anomalous contiguous intervalpoints.

The group processor 412 employs grouping rules that indicate specificsizes of groups for the stream, and generates a group size along withthe declared anomaly on G.X. The declared anomaly and its group size areread by the estimation/edit processor 413, which accesses the sourcesmetadata stores 414 to obtain the optimum estimation rule for theprovided group size. The estimation/edit processor 413 may then generateestimated interval data points corresponding to the anomalous intervaldata points by employing estimation techniques prescribed by the optimumestimation/edit rule. The estimation/edit processor 413 may then replacethe anomalous interval data points with the estimated interval datapoints and will tag the estimated interval data points as correspondingto anomalous data. The estimation/edit processor 413 may buffer datapoints (those estimated and those not declared anomalous) providedsubsequently for use in generating estimated interval data points for acurrent anomalous interval. The estimation/edit processor 413 mayretrieve previously stored data points (those estimated and those notdeclared anomalous) from the post VEE readings stores 416 for use ingenerating estimated interval data points for the current anomalousinterval. Stream data that is not anomalous is passed through the VEEprocessor 410 to bus ESTR.X as received over RCV.X and is tagged at notbeing anomalous.

As discussed above with reference to FIG. 3, the VEE configurationengine 420 may process one or more data streams at a periodic interval(as indicated by the timer 422), as a function of processing load withinthe NOC (as indicated by the load monitor 423) or both as a function ofprocessing load and a periodic interval. Accordingly, when triggered bythe timer 422, load monitor 423, or both the timer 422 and load monitor423, the rules processor 421 accesses data points corresponding to anext stream from the post VEE readings stores 416 and executes thefunctions noted above to optimize the next stream's detection andestimation/editing rules, which are then updated in the sources metadatastores 414. For applicable detection rules processing, the rulesprocessor 421 may also retrieve data points from streams of like devicetype, transport type, etc. in order to optimize detection thresholds.

Now turning to FIG. 5, a flow diagram is presented illustrating a methodaccording to the present invention for automatically configuringdetection rules for use by a VEE processor. Flow begins at block 502,when a trigger, as described above with reference to FIG. 4 is generatedindicating that a next stream is to be processed. Flow then proceeds toblock 504.

At block 504, a next stream identifier is selected for detection rulesoptimization. Flow then proceeds to block 506.

At block 506, attribute data (e.g., STREAM TYPE, TRANSPORT TYPE) isretrieved for the next stream identifier. Flow then proceeds to block508.

At block 506, adaptive detection rules for the next stream identifierare retrieved from the sources metadata stores. Flow then proceeds toblock 510.

At block 510, post VEE readings for streams having like stream type,transport type, or both are processed to generate optimum thresholds forthe retrieved adaptive detection rules. Flow then proceeds to block 512.

At block 512, the adaptive detection rules are updated with thegenerated optimum thresholds and these updated rules are provided to thesources metadata stores. Flow then proceeds to decision block 514.

At decision block 514, an evaluation is made to determine if all streamsprocessed by the NOC have been updated. If not, then flow proceeds toblock 504. If so, the flow proceeds to block 516.

At block 516, the method completes.

Now turning to FIG. 6, a flow diagram is presented detailing a methodaccording to the present invention for automatically configuringestimation rules for use by a VEE processor. Flow begins at block 602when a trigger, as described above with reference to FIGS. 4 and 5 isgenerated indicating that a next stream is to be processed. Flow thenproceeds to block 604.

At block 604, a next stream identifier is selected forestimation/editing rules optimization. Flow then proceeds to block 606.

At block 606, post VEE readings for the selected next stream identifierare retrieved from the post VEE readings stores. Flow then proceeds toblock 608.

At block 608, group types for all gap sizes associated with the selectedstream are created. Flow then proceeds to block 610.

At block 610 post VEE readings for the selected stream 304 that havebeen previously tagged as not being anomalous (i.e., they correspond tocorrect data) are employed to create a substantial number of createdanomalies for all group types associated with the selected stream. Flowthen proceeds to block 612.

At block 612, all estimation/editing rules employed by the NOC areemployed to generate estimated interval data for each of the createdanomalies. Flow then proceeds to block 614.

At block 614, all estimated interval data for created anomalies withineach group type are compared to true post VEE readings from which theanomalies were created, and error terms for each one of theestimation/editing rules employed are generated. Flow then proceeds tobock 616.

At block, 616, an estimation/editing rule for each group type isselected such that error between estimated interval data points and truepost VEE readings from which the anomalies were created is minimized.Flow then proceeds to block 618.

At block 618, the sources metadata stores is updated for the selectedstream, replacing former estimation/editing rules for each gap type withthe rules selected in block 616, thus providing increased VEE accuracyfor future interval data from the selected stream. Flow then proceeds todecision block 620.

At decision block 620, an evaluation is made to determine if all streamsprocessed by the NOC have been updated. If not, then flow proceeds toblock 604. If so, the flow proceeds to block 622.

At block 622, the method completes.

The present inventors note that estimated/edited interval data for anewly added stream experiencing anomalies may exhibit accuraciescommensurate with present day techniques. However, as more and moreinterval data for that stream is processed by the NOC (and stored in thepost VEE readings stores for auto optimization by the VEE configurationengine), accuracies will begin to markedly surpass present day systems.In embodiments that perform VEE associated with residential, office, andindustrial buildings, by the time three months of interval data has beenprocessed, the present invention will have configured optimum VEE rulesfor the stream.

Turning now to FIG. 7, a block diagram is presented illustrating anenergy baselining system 700 according to the present invention thatincludes an automated VEE rules configuration engine 731. The system 700includes representative metering sources 701 that generate and transmitthe streams 704 such as one or more AMR meters, one or more buildingautomation system (BAS) metering devices, and one or more other meteringdevices. The other metering devices may directly measure, generate, andtransmit streams 704, or they may relay the streams 704, as in the caseof a grid operator relaying streaming data to an ESCO. The streams 704may be transmitted to the NOC 710 by any well-known streaming mechanismsuch as, but not limited to, wired or wireless networks, radio frequencynetworks, cellular networks, satellite communications, etc.

The NOC 710 comprises a receiver 711 that is coupled to each of thestreams 704 and that performs the functions required to translatesignals corresponding to each of the streams 704 into data that issuitable for VEE processing within the NOC 710. Accordingly, thereceiver 711 is coupled to a VEE processor 712 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 704. RCV.1-RCV.N are coupled to the VEEprocessor 712. The VEE processor 712 is coupled to a facility modelprocessor 713 via bus CD. A service level agreement (SLA) may beprescribed for processing of the streams 704 by the VEE processor 712,and may establish a maximum processing time from receipt of the streams704 by the VEE processor 712 until post-VEE readings are provided to thefacility model processor 713. The facility model processor 713 may becoupled to a global model module 721, which may be internal or externalto the NOC 710. In the energy baselining system 700, the global modelmodule is depicted external to the NOC 710. A post-VEE readings stores716 is coupled to the VEE processor 312 via bus PV and to the facilitymodel processor 713. A training data stores 741 is coupled to thefacility model processor 713. A sources metadata stores 714 may becoupled to the VEE processor 712 via bus RULES. A VEE configurationengine 731 according to the present invention may be coupled to both thepost-VEE readings stores 716 and the sources metadata stores 714.

In operation, the NOC 710 may receive the energy consumption datastreams 704, and may be constrained by SLA to perform VEE functions onthe order of minutes. In one embodiment SLAs of approximately fiveminutes are contemplated, though other SLA processing times may beachieved according to the scope of the present disclosure. Accordingly,the streams 704 are received at the NOC 710 by the receiver 711, whichtranslates signals in the streams 704 into data suitable for executionof VEE functions by the VEE processor 712. In one embodiment, intervaltimes for the streams 704 may range from approximately one minute up toapproximately 24 hours.

As a next interval value is translated for a given stream 704, the VEEprocessor 712 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to the facility model processor 713 overbus CD. The next interval value is also provided to the post-VEEreadings stores 716 over bus PV, where it is available for functionsexecuted by the facility model processor 713, and may also be accessedby the VEE configuration engine 731. If the next interval value isdeemed an anomaly, then the VEE processor 712 may employ estimation andediting rules for the given stream 704 to replace (i.e., “edit”) thenext interval value with an estimated next interval value. The estimatednext interval value may be provided to the facility model processor 713over bus CD. The estimated next interval value is also provided to thepost-VEE readings stores 716 over bus PV, where it is available forfunctions executed by the facility model processor 713. Each of the datastreams 704 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 714 tothe VEE processor 712 over bus RULES.

When a new stream 704 is added, the VEE configuration engine 731generates a default set of detection, estimation, and editing rules inthe sources metadata stores 714 that corresponds to the new stream 704.Detection rules may comprise, but are not limited to, gap detection,zero value detection, negative reading detection, high-read (i.e., overthreshold) detection, threshold setting, etc. Estimation rules maycomprise, but are not limited to, average of N preceding and Msubsequent interval values, replacement with values from previous day ofthe week, linear interpolation employing N preceding and M subsequentinterval values, replace with data from same time previous week, replacewith data from same time last week plus straight line interpolatedconstant, replace with data from same time previous day plus straightline interpolated constant, replace with average binned data fromprevious month (e.g., average of all 10:05 Monday interval values),replace with median binned data from previous month (e.g., median of all10:05 Monday interval values), etc.

The VEE configuration engine 731 thereafter periodically accesses thepost VEE readings stores 714 to analyze post VEE readings for the newstream 704 and for all other streams 704 processed by the NOC 710 to 1)adjust detection rules, 2) to group gaps in each stream 704 according togap size (i.e. duration of contiguous anomalous intervals in each stream704), and 3) to assign optimum estimation and editing rules for each gapsize group in each of the streams 704. In one embodiment, the VEEconfiguration engine 731 performs these noted functions serially on onestream 704 at a time, until all streams 704 have been analyzed and theircorresponding VEE rules have been updated. In one embodiment, the VEEconfiguration engine 731 performs its analysis and rules optimizationfunctions continuously. In another embodiment, the VEE configurationengine 731 performs its analysis and rules optimization functions as afunction of system processing load, where processing loads less than athreshold value enables the VEE configuration engine 731 to execute itsfunctions, and processing loads greater than the threshold precludesexecution of its functions. In one embodiment, the threshold is 50percent. In a further embodiment, the VEE configuration engine 731cycles serially through all the streams 704 every 24 hours. In yet afurther embodiment, the VEE configuration engine 731 cycles seriallythrough all the streams 704 every seven days. In yet another embodiment,the VEE configuration engine 731 cycles serially through all the streamsonce a month.

After a given stream 704 has been processed, the VEE configurationengine 731 updates VEE rules for the stream 704 in the sources metadatastores to replace previous VEE rules with updated, optimized VEE rules.

The global model module 721 may be configured as an energy managementcontrol device, described in further detail below, or as an energyprofile evaluation device. As an energy profile evaluation device, theglobal model module 721 may include a display such as, but not limitedto, a wall-mounted display, a desktop display, a laptop display, atablet display, or a mobile phone display.

In operation, the energy baselining system 700 according to the presentinvention may be employed for purposes of generating an accuratefacility energy consumption model for a given facility or building, orgroups of buildings having time increments of two hours or less (i.e.,1-hour increments, 30-minute increments, 15-minute increments, etc.),and for purposes of employing the energy consumption model to compareenergy consumption data derived from the energy consumption data (asedited by the VEE processor 712) provided over CD and post VEE readingswith baseline energy consumption data derived from training dataprovided from the training data stores 741, and further to forecastenergy consumption for future dates. In one embodiment, the trainingdata comprises interval based energy consumption data for the facility(or groups) for a previous time period. In one embodiment, the durationof the time period is three months. In another embodiment, the durationof the time period is one year. The facility energy consumption model,as will be described in more detail below, may comprise a lower boundcomponent of building energy consumption as a function of outsidetemperature, an upper bound component of building energy consumption asa function of outside temperature, etc. The aforementioned componentsare generated by the facility model processor 713 based upon the valuesof historical energy consumption training data provided from thetraining data stores 741. In one embodiment, the aforementionedcomponents may be generated based upon the values of the training dataand progressively revised (i.e., iterated) based upon the values ofenergy consumption data provided over bus CD. The facility modelprocessor 713 may normalize the baseline energy consumption data derivedfrom the training data to remove the effects of outside temperature,resulting in normalized baseline energy consumption data. The facilitymodel processor 713 may also normalize the energy consumption dataprovided over CD and from post VEE readings to remove the effects ofoutside temperature, resulting in normalized energy consumption data.

Thereafter, the global model module 721 may generate and displaycomparisons of the normalized energy consumption data with thenormalized baseline energy consumption data for purposes of enabling abuilding manager to evaluate the efficacy of energy efficiencyimprovements performed on the facility subsequent to generation of thetraining data and prior to generation of the energy consumption data. Inanother embodiment, the global model module 721 may generate comparisonsof the normalized energy consumption with the normalized baseline energyconsumption data for purposes of enabling a building manager toretroactively visualize the efficacy of energy efficiency improvementsperformed on the facility prior to generation of the training data andsubsequent to generation of the energy consumption data. In anadditional embodiment, the global model module 721 may generatecomparisons of the normalized energy consumption data with thenormalized baseline energy consumption data for purposes of enabling abuilding manager to detect abnormal daily energy usage for the facilityby comparing the normalized energy consumption data with the normalizedbaseline energy consumption data. In such a comparison, the global modelmodule 721 may visually display an approximate expected range ofnormalized energy consumption values for a given time period as afunction of the normalized baseline energy consumption data. In yetanother embodiment, the global model module 721 may display theforecasted energy consumption for purposes of enabling a buildingmanager to plan future energy acquisitions.

Numerous other embodiments may be configured for the global model module721 as need arises for comparison of normalized energy consumption datawith normalized baseline energy consumption data on a daily, weekly,monthly, yearly, etc. level, where the building manager may be presentedwith an expected normalized energy consumption profile (based on thenormalized baseline data and the aforementioned components) along withwhat the given building actually consumed (based on the normalizedconsumption data) in the past, the near-real time present, or projectedfor the future. As noted above, the energy baselining system 700according to the present invention may be employed to perform the abovenoted functions for a plurality of buildings.

The VEE processor 712, facility model processor 713, VEE configurationengine 731, and global model module 721 may comprise hardware, or acombination of hardware and software, configured to perform thefunctions described above. In one embodiment, the VEE processor 712,facility model processor 713, VEE configuration engine 731, and globalmodel module 721 may comprise a plurality of microprocessors or othersuitable central processing units (CPU) (not shown) coupled to acorresponding plurality of transitory random access memory (not shown)and/or a plurality of non-transitory read-only memory (not shown) withinwhich application programs (i.e., software) are disposed that, whenexecuted by the microprocessors/CPUs, perform the functions describedabove. The stores 714, 716, 741 may be disposed as conventionaltransitory or non-transitory data storage mechanisms and the buseswithin the system 700 may comprise conventional wired or wirelesstechnology buses for transmission and reception of data including, butnot limited to, direct wired (e.g., SATA), cellular, BLUETOOTH®, Wi-Fi,Ethernet, and the internet.

Turning now to FIG. 8, a block diagram is presented illustrating demandresponse dispatch prediction system 800 according to the presentinvention that includes an automated VEE rules configuration engine 831.The system 800 includes representative metering sources 801 thatgenerate and transmit the streams 804 such as one or more AMR meters,one or more building automation system (BAS) metering devices, and oneor more other metering devices. The other metering devices may directlymeasure, generate, and transmit streams 804, or they may relay thestreams 804, as in the case of a grid operator relaying streaming datato an ESCO. The streams 804 may be transmitted to the NOC 810 by anywell-known streaming mechanism such as, but not limited to, wired orwireless networks, radio frequency networks, cellular networks,satellite communications, etc.

The NOC 810 comprises a receiver 811 that is coupled to each of thestreams 804 and that performs the functions required to translatesignals corresponding to each of the streams 804 into data that issuitable for VEE processing within the NOC 810. Accordingly, thereceiver 811 is coupled to a VEE processor 812 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 804. RCV.1-RCV.N are coupled to the VEEprocessor 812. The VEE processor 812 is coupled to a dispatch predictionelement 813 via bus CD that is configured to predict a first future timewhen a demand response dispatch order may be received from a dispatchauthority such as an ISO, RTO, or utility. The dispatch order mayspecify, among other things, a second future time to execute a demandresponse program event along with a value of energy that is to be shedby participants in a corresponding demand response program. A servicelevel agreement (SLA) may be prescribed for processing of the streams804 by the VEE processor 812, and may establish a maximum processingtime from receipt of the streams 804 by the VEE processor 812 untilpost-VEE readings are provided to the dispatch prediction element 813.The dispatch prediction element 813 may be coupled to a dispatchcontroller 821, which may be internal or external to the NOC 810. In thedispatch prediction system 800, the dispatch controller 821 is depictedexternal to the NOC 810. A post-VEE readings stores 816 is coupled tothe VEE processor 312 via bus PV and to the dispatch prediction element813. A weather data stores 841 is coupled to the dispatch predictionelement 813. A sources metadata stores 814 may be coupled to the VEEprocessor 812 via bus RULES. A VEE configuration engine 831 according tothe present invention may be coupled to both the post-VEE readingsstores 816 and the sources metadata stores 814.

In operation, the demand response dispatch prediction system 800 isemployed to estimate cumulative energy consumption as a function of thepredicted outside temperatures occurring in a timeline for a pluralityof buildings corresponding to the streams 804 that participate in thedemand response program, where VEE functions according to the presentinvention are utilized in generation of a cumulative energy consumptiontimeline. It is noted that, according to features of the presentinvention disclosed herein, the predicted energy consumption timelinemay be employed to anticipate reception of a dispatch order to a finerlevel of granularity than that which has heretofore been provided, dueto the increased accuracy of near real time energy consumption data.Thus, estimated reception of a dispatch order may be finely tuned.Advantageously, by utilizing the present invention to determine a timewhen a dispatch threshold of energy consumption will be reached due tooutside temperature, an energy services company or other demand responsedispatch control entity may be provided with, say, additional hours forpreparation of dispatch control actions.

The system 800 generates a predicted dispatch time that is provided tothe dispatch controller 821 for preparation of actions required tocontrol each of the one or more buildings to optimally shed the energyspecified in the dispatch order, upon reception of the dispatch order.

To predict the dispatch time, the dispatch prediction element 813receives energy consumption data via bus CD and the post VEE readingsstores 816. The prediction element 813 also accesses the weather stores841 to obtain future outside temperatures corresponding to each of theplurality of buildings for a specified future time period. The dispatchpredication element 813 then builds a cumulative future energyconsumption timeline for all of the buildings using the outsidetemperatures as inputs to energy consumption models according to thepresent invention for all of the buildings. The dispatch predictionelement 813 then processes the cumulative energy consumption timeline todetermine a time when cumulative energy consumption increases as tocross a specified threshold known to trigger a demand response programevent. The point at which consumption crosses the specified threshold istagged as a dispatch time. From the dispatch time, the dispatchprediction element 813 may utilize demand response program contract datastored therein to calculate a predicted dispatch reception time,typically 24 hours prior to commencement of the demand response programevent. The dispatch reception time is provided to the dispatch controlelement 821 to allow for commencement of dispatch actions at a timehaving greater accuracy than that which has heretofore been provided.

The NOC 810 may receive the energy consumption data streams 804, and maybe constrained by SLA to perform VEE functions on the order of minutes.In one embodiment SLAs of approximately five minutes are contemplated,though other SLA processing times may be achieved according to the scopeof the present disclosure. Accordingly, the streams 804 are received atthe NOC 810 by the receiver 811, which translates signals in the streams804 into data suitable for execution of VEE functions by the VEEprocessor 812. In one embodiment, interval times for the streams 804 mayrange from approximately one minute up to approximately 24 hours.

As a next interval value is translated for a given stream 804, the VEEprocessor 812 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to the dispatch prediction element 813over bus CD. The next interval value is also provided to the post-VEEreadings stores 816 over bus PV, where it is available for functionsexecuted by the facility model processor 813, and may also be accessedby the VEE configuration engine 831. If the next interval value isdeemed an anomaly, then the VEE processor 812 may employ estimation andediting rules for the given stream 804 to replace (i.e., “edit”) thenext interval value with an estimated next interval value. The estimatednext interval value may be provided to the dispatch prediction element813 over bus CD. The estimated next interval value is also provided tothe post-VEE readings stores 816 over bus PV, where it is available forfunctions executed by the dispatch prediction element 813. Each of thedata streams 804 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 814 tothe VEE processor 412 over bus RULES.

When a new stream 804 is added, the VEE configuration engine 831generates a default set of detection, estimation, and editing rules inthe sources metadata stores 814 that corresponds to the new stream 804.Detection rules may comprise, but are not limited to, gap detection,zero value detection, negative reading detection, high-read (i.e., overthreshold) detection, threshold setting, etc. Estimation rules maycomprise, but are not limited to, average of N preceding and Msubsequent interval values, replacement with values from previous day ofthe week, linear interpolation employing N preceding and M subsequentinterval values, replace with data from same time previous week, replacewith data from same time last week plus straight line interpolatedconstant, replace with data from same time previous day plus straightline interpolated constant, replace with average binned data fromprevious month (e.g., average of all 10:05 Monday interval values),replace with median binned data from previous month (e.g., median of all10:05 Monday interval values), etc.

The VEE configuration engine 831 thereafter periodically accesses thepost VEE readings stores 814 to analyze post VEE readings for the newstream 804 and for all other streams 804 processed by the NOC 810 to 1)adjust detection rules, 2) to group gaps in each stream 804 according togap size (i.e. duration of contiguous anomalous intervals in each stream804), and 3) to assign optimum estimation and editing rules for each gapsize group in each of the streams 804. In one embodiment, the VEEconfiguration engine 831 performs these noted functions serially on onestream 804 at a time, until all streams 804 have been analyzed and theircorresponding VEE rules have been updated. In one embodiment, the VEEconfiguration engine 831 performs its analysis and rules optimizationfunctions continuously. In another embodiment, the VEE configurationengine 831 performs its analysis and rules optimization functions as afunction of system processing load, where processing loads less than athreshold value enables the VEE configuration engine 831 to execute itsfunctions, and processing loads greater than the threshold precludesexecution of its functions. In one embodiment, the threshold is 50percent. In a further embodiment, the VEE configuration engine 831cycles serially through all the streams 804 every 24 hours. In yet afurther embodiment, the VEE configuration engine 831 cycles seriallythrough all the streams 804 every seven days. In yet another embodiment,the VEE configuration engine 831 cycles serially through all the streamsonce a month.

After a given stream 804 has been processed, the VEE configurationengine 831 updates VEE rules for the stream 804 in the sources metadatastores to replace previous VEE rules with updated, optimized VEE rules.

The VEE processor 812, dispatch prediction element 813, VEEconfiguration engine 831, and dispatch controller 821 may comprisehardware, or a combination of hardware and software, configured toperform the functions described above. In one embodiment, the VEEprocessor 812, dispatch prediction element 813, VEE configuration engine831, and dispatch controller 821 may comprise a plurality ofmicroprocessors or other suitable central processing units (CPU) (notshown) coupled to a corresponding plurality of transitory random accessmemory (not shown) and/or a plurality of non-transitory read-only memory(not shown) within which application programs (i.e., software) aredisposed that, when executed by the microprocessors/CPUs, perform thefunctions described above. The stores 814, 816, 841 may be disposed asconventional transitory or non-transitory data storage mechanisms andthe buses within the system 800 may comprise conventional wired orwireless technology buses for transmission and reception of dataincluding, but not limited to, direct wired (e.g., SATA), cellular,BLUETOOTH®, Wi-Fi, Ethernet, and the internet.

Referring now to FIG. 9, a block diagram is presented featuring a brownout prediction system 900 according to the present invention thatincludes an automated VEE rules configuration engine 931. The system 900includes representative metering sources 901 that generate and transmitthe streams 904 such as one or more AMR meters, one or more buildingautomation system (BAS) metering devices, and one or more other meteringdevices. The other metering devices may directly measure, generate, andtransmit streams 904, or they may relay the streams 904, as in the caseof a grid operator relaying streaming data to an ESCO. The streams 904may be transmitted to the NOC 910 by any well-known streaming mechanismsuch as, but not limited to, wired or wireless networks, radio frequencynetworks, cellular networks, satellite communications, etc.

The NOC 910 comprises a receiver 911 that is coupled to each of thestreams 904 and that performs the functions required to translatesignals corresponding to each of the streams 904 into data that issuitable for VEE processing within the NOC 910. Accordingly, thereceiver 911 is coupled to a VEE processor 912 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 904. RCV.1-RCV.N are coupled to the VEEprocessor 912. The VEE processor 912 is coupled to a peak predictionelement 913 via bus CD that that is configured to predict a future brownout time when energy consumption on a grid controlled by an ISO, RTO, orutility, may exceed normal production capacity, and would therebyrequire exceptional measures known in the art to increase energycapacity. A service level agreement (SLA) may be prescribed forprocessing of the streams 904 by the VEE processor 912, and mayestablish a maximum processing time from receipt of the streams 904 bythe VEE processor 912 until post-VEE readings are provided to thedispatch prediction element 913. The peak prediction element 913 may becoupled to a peak controller 921, which may be internal or external tothe NOC 910. In the brown out prediction system 900, the peak controller921 is depicted external to the NOC 910. A post-VEE readings stores 916is coupled to the VEE processor 312 via bus PV and to the peakprediction element 913. A weather data stores 941 is coupled to thedispatch prediction element 913. A sources metadata stores 914 may becoupled to the VEE processor 912 via bus RULES. A VEE configurationengine 931 according to the present invention may be coupled to both thepost-VEE readings stores 916 and the sources metadata stores 914. Theweather stores 941 comprises weather predictions that include outsidetemperatures corresponding to buildings corresponding to the streams904. The weather stores 941 may be located on site, or may be locatedremotely and accessed via conventional networking technologies.

In operation, the brown out prediction system 900 is employed toestimate cumulative energy consumption on a grid as a function of thepredicted outside temperatures occurring in a timeline for a pluralityof buildings corresponding to the streams 904 within the grid, where VEEfunctions according to the present invention are utilized in generationof a cumulative energy consumption timeline. It is noted that, accordingto features of the present invention disclosed herein, the predictedenergy consumption timeline may be employed to anticipate a brown outtime to a finer level of granularity than that which has heretofore beenprovided, due to the increased accuracy of near real time energyconsumption data. Thus, the system 900 generates a predicted brown outtime that is provided to the peak controller 921 for preparation ofexceptional measures required to manage peak consumption, such asinitialization of surge production plants, etc.

To predict the brown out time, the peak prediction element 913 receivesenergy consumption data via bus CD and the post VEE readings stores 916.The prediction element 913 also accesses the weather stores 941 toobtain future outside temperatures corresponding to each of theplurality of buildings for a specified future time period. The peakpredication element 913 then builds a cumulative future energyconsumption timeline for all of the buildings using the outsidetemperatures as inputs to energy consumption models according to thepresent invention for all of the buildings. The peak prediction element913 then processes the cumulative energy consumption timeline todetermine a time when cumulative energy consumption increases as tocross a specified threshold known to trigger exceptional measures topreclude a brown out. The point at which consumption crosses thespecified threshold is tagged as a brown out time. The brown out time isthen provided to the peak controller 921, which in triggers theexceptional measures to preclude a brown out.

The NOC 910 may receive the energy consumption data streams 904, and maybe constrained by SLA to perform VEE functions on the order of minutes.In one embodiment SLAs of approximately five minutes are contemplated,though other SLA processing times may be achieved according to the scopeof the present disclosure. Accordingly, the streams 904 are received atthe NOC 910 by the receiver 911, which translates signals in the streams904 into data suitable for execution of VEE functions by the VEEprocessor 912. In one embodiment, interval times for the streams 904 mayrange from approximately one minute up to approximately 24 hours.

As a next interval value is translated for a given stream 904, the VEEprocessor 912 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to the dispatch prediction element 913over bus CD. The next interval value is also provided to the post-VEEreadings stores 916 over bus PV, where it is available for functionsexecuted by the facility model processor 913, and may also be accessedby the VEE configuration engine 931. If the next interval value isdeemed an anomaly, then the VEE processor 912 may employ estimation andediting rules for the given stream 904 to replace (i.e., “edit”) thenext interval value with an estimated next interval value. The estimatednext interval value may be provided to the peak prediction element 913over bus CD. The estimated next interval value is also provided to thepost-VEE readings stores 916 over bus PV, where it is available forfunctions executed by the peak prediction element 913. Each of the datastreams 904 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 914 tothe VEE processor 912 over bus RULES.

When a new stream 904 is added, the VEE configuration engine 931generates a default set of detection, estimation, and editing rules inthe sources metadata stores 914 that corresponds to the new stream 904.Detection rules may comprise, but are not limited to, gap detection,zero value detection, negative reading detection, high-read (i.e., overthreshold) detection, threshold setting, etc. Estimation rules maycomprise, but are not limited to, average of N preceding and Msubsequent interval values, replacement with values from previous day ofthe week, linear interpolation employing N preceding and M subsequentinterval values, replace with data from same time previous week, replacewith data from same time last week plus straight line interpolatedconstant, replace with data from same time previous day plus straightline interpolated constant, replace with average binned data fromprevious month (e.g., average of all 10:05 Monday interval values),replace with median binned data from previous month (e.g., median of all10:05 Monday interval values), etc.

The VEE configuration engine 931 thereafter periodically accesses thepost VEE readings stores 914 to analyze post VEE readings for the newstream 904 and for all other streams 904 processed by the NOC 910 to 1)adjust detection rules, 2) to group gaps in each stream 904 according togap size (i.e. duration of contiguous anomalous intervals in each stream904), and 3) to assign optimum estimation and editing rules for each gapsize group in each of the streams 904. In one embodiment, the VEEconfiguration engine 931 performs these noted functions serially on onestream 904 at a time, until all streams 904 have been analyzed and theircorresponding VEE rules have been updated. In one embodiment, the VEEconfiguration engine 931 performs its analysis and rules optimizationfunctions continuously. In another embodiment, the VEE configurationengine 931 performs its analysis and rules optimization functions as afunction of system processing load, where processing loads less than athreshold value enables the VEE configuration engine 931 to execute itsfunctions, and processing loads greater than the threshold precludesexecution of its functions. In one embodiment, the threshold is 50percent. In a further embodiment, the VEE configuration engine 931cycles serially through all the streams 904 every 24 hours. In yet afurther embodiment, the VEE configuration engine 931 cycles seriallythrough all the streams 904 every seven days. In yet another embodiment,the VEE configuration engine 931 cycles serially through all the streamsonce a month.

After a given stream 904 has been processed, the VEE configurationengine 931 updates VEE rules for the stream 904 in the sources metadatastores to replace previous VEE rules with updated, optimized VEE rules.

The VEE processor 912, peak prediction element 913, VEE configurationengine 931, and peak controller 921 may comprise hardware, or acombination of hardware and software, configured to perform thefunctions described above. In one embodiment, the VEE processor 912,peak prediction element 913, VEE configuration engine 931, and peakcontroller 921 may comprise a plurality of microprocessors or othersuitable central processing units (CPU) (not shown) coupled to acorresponding plurality of transitory random access memory (not shown)and/or a plurality of non-transitory read-only memory (not shown) withinwhich application programs (i.e., software) are disposed that, whenexecuted by the microprocessors/CPUs, perform the functions describedabove. The stores 914, 916, 941 may be disposed as conventionaltransitory or non-transitory data storage mechanisms and the buseswithin the system 900 may comprise conventional wired or wirelesstechnology buses for transmission and reception of data including, butnot limited to, direct wired (e.g., SATA), cellular, BLUETOOTH®, Wi-Fi,Ethernet, and the internet.

Finally referring to FIG. 10, a block diagram is presented showing anautomated building control system 1000 according to the presentinvention that includes an automated VEE rules configuration engine1031. The system 1000 includes representative metering sources 1001 thatgenerate and transmit the streams 1004 such as one or more AMR meters,one or more building automation system (BAS) metering devices, and oneor more other metering devices, where the streams 1004 correspond to aplurality of metering points 1001 within one or more buildings that areunder control of the system 1000. The metering points 1001 may directlymeasure, generate, and transmit streams 1004, or they may relay thestreams 1004, as in the case of a grid operator relaying streaming datato an ESCO. The streams 1004 may be transmitted to the NOC 1010 by anywell-known streaming mechanism such as, but not limited to, wired orwireless networks, radio frequency networks, cellular networks,satellite communications, etc.

The NOC 1010 comprises a receiver 1011 that is coupled to each of thestreams 1004 and that performs the functions required to translatesignals corresponding to each of the streams 1004 into data that issuitable for VEE processing within the NOC 1010. Accordingly, thereceiver 1011 is coupled to a VEE processor 1012 via buses RCV.1-RCV.N,each of which comprises received and translated data for a correspondingone of the data streams 1004. RCV.1-RCV.N are coupled to the VEEprocessor 1012. The VEE processor 1012 is coupled to a buildingcontroller 1013 via bus CD. A service level agreement (SLA) may beprescribed for processing of the streams 1004 by the VEE processor 1012,and may establish a maximum processing time from receipt of the streams1004 by the VEE processor 1012 until post-VEE readings are provided tothe building controller 1013. The building controller 1013 may becoupled to a one or more controllable building elements 1021 within theone or more buildings, or that correspond to the one or more buildings.The building elements may include, but are not limited to, lightingsystem sensors and components, heating system sensors and components,air conditioning system sensors and components, security system sensorsand components, transport devices, traffic control devices, powergeneration and distribution sensors and components, etc. A post-VEEreadings stores 1016 is coupled to the VEE processor 312 via bus PV andto the building controller 1013. A weather data stores 1041 is coupledto the building controller 1013. A sources metadata stores 1014 may becoupled to the VEE processor 1012 via bus RULES. A VEE configurationengine 1031 according to the present invention may be coupled to boththe post-VEE readings stores 1016 and the sources metadata stores 1014.

In operation, the NOC 1010 may receive the energy consumption datastreams 1004, and may be constrained by SLA to perform VEE functions onthe order of minutes. In one embodiment SLAs of approximately fiveminutes are contemplated, though other SLA processing times may beachieved according to the scope of the present disclosure. Accordingly,the streams 1004 are received at the NOC 1010 by the receiver 1011,which translates signals in the streams 1004 into data suitable forexecution of VEE functions by the VEE processor 1012. In one embodiment,interval times for the streams 1004 may range from approximately oneminute up to approximately 24 hours.

As a next interval value is translated for a given stream 1004, the VEEprocessor 1012 may employ detection rules to determine if the nextinterval value comprises an anomaly. If the next interval value is notan anomaly, then it is provided to the facility model processor 1013over bus CD. The next interval value is also provided to the post-VEEreadings stores 1016 over bus PV, where it is available for functionsexecuted by the building controller 1013, and may also be accessed bythe VEE configuration engine 1031. If the next interval value is deemedan anomaly, then the VEE processor 1012 may employ estimation andediting rules for the given stream 1004 to replace (i.e., “edit”) thenext interval value with an estimated next interval value. The estimatednext interval value may be provided to the building controller 1013 overbus CD. The estimated next interval value is also provided to thepost-VEE readings stores 1016 over bus PV, where it is available forfunctions executed by the facility model processor 1013. Each of thedata streams 1004 have a corresponding set of detection, estimation, andediting rules, which are provided by the sources metadata stores 1014 tothe VEE processor 1012 over bus RULES.

When a new stream 1004 is added, the VEE configuration engine 1031generates a default set of detection, estimation, and editing rules inthe sources metadata stores 1014 that corresponds to the new stream1004. Detection rules may comprise, but are not limited to, gapdetection, zero value detection, negative reading detection, high-read(i.e., over threshold) detection, threshold setting, etc. Estimationrules may comprise, but are not limited to, average of N preceding and Msubsequent interval values, replacement with values from previous day ofthe week, linear interpolation employing N preceding and M subsequentinterval values, replace with data from same time previous week, replacewith data from same time last week plus straight line interpolatedconstant, replace with data from same time previous day plus straightline interpolated constant, replace with average binned data fromprevious month (e.g., average of all 10:05 Monday interval values),replace with median binned data from previous month (e.g., median of all10:05 Monday interval values), etc.

The VEE configuration engine 1031 thereafter periodically accesses thepost VEE readings stores 1014 to analyze post VEE readings for the newstream 1004 and for all other streams 1004 processed by the NOC 1010to 1) adjust detection rules, 2) to group gaps in each stream 1004according to gap size (i.e. duration of contiguous anomalous intervalsin each stream 1004), and 3) to assign optimum estimation and editingrules for each gap size group in each of the streams 1004. In oneembodiment, the VEE configuration engine 1031 performs these notedfunctions serially on one stream 1004 at a time, until all streams 1004have been analyzed and their corresponding VEE rules have been updated.In one embodiment, the VEE configuration engine 1031 performs itsanalysis and rules optimization functions continuously. In anotherembodiment, the VEE configuration engine 1031 performs its analysis andrules optimization functions as a function of system processing load,where processing loads less than a threshold value enables the VEEconfiguration engine 1031 to execute its functions, and processing loadsgreater than the threshold precludes execution of its functions. In oneembodiment, the threshold is 50 percent. In a further embodiment, theVEE configuration engine 1031 cycles serially through all the streams1004 every 24 hours. In yet a further embodiment, the VEE configurationengine 1031 cycles serially through all the streams 1004 every sevendays. In yet another embodiment, the VEE configuration engine 1031cycles serially through all the streams once a month.

After a given stream 1004 has been processed, the VEE configurationengine 1031 updates VEE rules for the stream 1004 in the sourcesmetadata stores to replace previous VEE rules with updated, optimizedVEE rules.

In operation, the building control system 1000 according to the presentinvention may be employed for purposes of generating an accurate energyconsumption model for the building (or plurality of buildings) havingtime increments of two hours or less (i.e., 1-hour increments, 30-minuteincrements, 15-minute increments, etc.). The building controller 1013may develop and dynamically revise the energy consumption model based onenergy consumption data provided over bus CD and from post VEE readingsto control overall energy consumption of systems within the building (orplurality of buildings) by minimizing peak demand and surges whilemaintaining optimum comfort and operating conditions. The energyconsumption may be controlled by scheduling run times for one or more ofthe building elements 1021 by techniques known in the art to minimizepeak demand and time of use charges, or to achieve energy efficiencyincentives. The building controller 1013 may additionally forecastenergy consumption for future times by employing data (as edited by theVEE processor 1012) provided over CD and post VEE readings inconjunction with weather forecast data provided by the weather datastores 1041 and may schedule run times for one or more of the buildingelements 1021 as a function of the weather forecast data.

The building controller 1013 may optimize the energy consumption of thebuilding for comfort purposes prior to or during demand response programevents (e.g., load shedding), to preclude time of use charges, or toachieve energy reduction incentives. The building controller 1013 mayemploy the post VEE readings and near real-time energy consumption dataprovided via bus CD, to develop modeling components indicative of adaily occupancy level for the building (or plurality of buildings) andmay perform comfort control functions, security control functions,resource control functions, market control functions, advertisingfunctions, and other control functions based upon the determined dailyoccupancy level, where the daily occupancy level is determinedexclusively from the energy consumption data, outside temperature data,and the aforementioned model components.

The VEE processor 1012, building controller 1013, VEE configurationengine 1031, and building elements 1021 may comprise hardware, or acombination of hardware and software, configured to perform thefunctions described above. In one embodiment, the VEE processor 1012,building controller 1013, VEE configuration engine 1031, and buildingelements 1021 may comprise a plurality of microprocessors or othersuitable central processing units (CPU) (not shown) coupled to acorresponding plurality of transitory random access memory (not shown)and/or a plurality of non-transitory read-only memory (not shown) withinwhich application programs (i.e., software) are disposed that, whenexecuted by the microprocessors/CPUs, perform the functions describedabove. The stores 1014, 1016, 1041 may be disposed as conventionaltransitory or non-transitory data storage mechanisms and the buseswithin the system 1000 may comprise conventional wired or wirelesstechnology buses for transmission and reception of data including, butnot limited to, direct wired (e.g., SATA), cellular, BLUETOOTH®, Wi-Fi,Ethernet, and the internet.

Advantageously, the present invention provides for markedly increasedreal time and near real time VEE accuracies through simultaneousoptimization of detection, estimation, and editing rules as a functionthe duration of anomalies for specific streaming devices and transporttechnologies, while eliminating costly data analyst requirements.

The present invention furthermore may be employed to perform VEEfunctions corresponding to other commodities such as, but not limitedto, water, natural gas, coal, nuclear energy, fuel, etc. where usagedata is obtained from streaming sources and where increased real time ornear real time accuracy of usage data is desired.

Portions of the present invention and corresponding detailed descriptionare presented in terms of software, or algorithms and symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the ones by which those ofordinary skill in the art effectively convey the substance of their workto others of ordinary skill in the art. An algorithm, as the term isused here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, a microprocessor, a central processingunit, or similar electronic computing device, that manipulates andtransforms data represented as physical, electronic quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Note also that the software implemented aspects of the invention aretypically encoded on some form of program storage medium or implementedover some type of transmission medium. The program storage medium may beelectronic (e.g., read only memory, flash read only memory, electricallyprogrammable read only memory), random access memory magnetic (e.g., afloppy disk or a hard drive) or optical (e.g., a compact disk read onlymemory, or “CD ROM”), and may be read only or random access. Similarly,the transmission medium may be metal traces, twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The invention is not limited by these aspects of any givenimplementation.

The particular embodiments disclosed above are illustrative only, andthose skilled in the art will appreciate that they can readily use thedisclosed conception and specific embodiments as a basis for designingor modifying other structures for carrying out the same purposes of thepresent invention, and that various changes, substitutions andalterations can be made herein without departing from the scope of theinvention as set forth by the appended claims.

What is claimed is:
 1. An energy control system, comprising: a facilitymodel processor, that receives interval based energy consumption streamscorresponding to a facility, and that develops and maintainsweather-normalized baseline energy consumption data for said facility,wherein said weather-normalized baseline energy consumption data isderived from training data for said facility; a post VEE readings datastores, that provides tagged energy consumption data sets that are eachassociated with a corresponding one of said interval based energyconsumption streams, each of said tagged energy consumption data setscomprising groups of contiguous interval values tagged as having beenvalidated, wherein said groups correspond to correct data; a VEEconfiguration engine, that reads said post VEE readings data stores uponinitiation of an event and, for said each of said tagged energyconsumption data sets, that creates anomalies having different durationsusing only said groups of contiguous interval values, and that generatesestimates for said anomalies by employing estimation techniques for eachof said different durations and, for said each of said differentdurations, that selects a corresponding one of said estimationtechniques for subsequent employment when performing VEE of subsequentenergy consumption data associated with said each of said differentdurations for said corresponding one of said interval based energyconsumption streams; and a global model module, that receives saidweather-normalized baseline energy consumption data and post VEEreadings data, and that develops an energy consumption model based onsaid weather-normalized baseline energy consumption data and said postVEE readings data, and that controls overall energy consumption withinsaid facility based on said energy consumption model by scheduling runtimes of one or more building elements.
 2. The energy control system asrecited in claim 1, wherein said corresponding one of said estimationtechniques is selected because its corresponding estimates are moreaccurate than estimates generated by remaining ones of said estimationtechniques.
 3. The energy control system as recited in claim 1, whereinsaid VEE configuration engine updates estimation rules for all of saidinterval based energy consumption streams in parallel with real timeprocessing of said interval based energy consumption streams within anetwork operations center, and wherein said interval based energyconsumption streams are processed serially by said VEE configurationengine.
 4. The energy control system as recited in claim 3, wherein VEEfor said interval based energy consumption streams must be completedwithin five minutes.
 5. The energy control system as recited in claim 1,wherein said estimation techniques comprises 10 or more estimationtechniques.
 6. The energy control system as recited in claim 1, whereinsaid event is signaled by a timer set to a periodic interval.
 7. Theenergy control system as recited in claim 6, wherein said periodicinterval comprises 24 hours.
 8. An energy control system, comprising: afacility model processor, that receives interval based energyconsumption streams corresponding to a facility, and that develops andmaintains weather-normalized baseline energy consumption data for saidfacility, wherein said weather-normalized baseline energy consumptiondata is derived from training data for said facility; a sources metadatastores, that provides validation, estimation, and editing (VEE) rulescorresponding to each of the interval based energy consumption streamsfor performing VEE; a post VEE readings data stores, that providestagged energy consumption data sets that are each associated with acorresponding one of said interval based energy consumption streams,each of said tagged energy consumption data sets comprising groups ofcontiguous interval values tagged as having been validated, wherein saidgroups correspond to correct data; a VEE configuration engine, thatreads said post VEE readings data stores upon initiation of an eventand, for said each of said tagged energy consumption data sets, thatcreates anomalies having different durations using only said groups ofcontiguous interval values, and that generates estimates for saidanomalies by employing estimation techniques for each of said differentdurations and, for said each of said different durations, that selects acorresponding one of said estimation techniques for subsequentemployment when performing VEE of subsequent energy consumption dataassociated with said each of said different durations for saidcorresponding one of said interval based energy consumption streams; anda global model module, that receives said weather-normalized baselineenergy consumption data and post VEE readings data, and that develops anenergy consumption model based on said weather-normalized baselineenergy consumption data and said post VEE readings data, and thatcontrols overall energy consumption within said facility based on saidenergy consumption model by scheduling run times of one or more buildingelements.
 9. The energy control system as recited in claim 8, whereinsaid corresponding one of said estimation techniques is selected becauseits corresponding estimates are more accurate than estimates generatedby remaining ones of said estimation techniques.
 10. The energy controlsystem as recited in claim 8, wherein said VEE configuration engineupdates said sources metadata stores with estimation rules for all ofthe interval based energy consumption streams in parallel with real timeprocessing of the interval based energy consumption streams within anetwork operations center, and wherein the interval based energyconsumption streams are processed serially by said VEE configurationengine.
 11. The energy control system as recited in claim 10, whereinVEE for the interval based energy consumption streams must be completedwithin five minutes.
 12. The energy control system as recited in claim8, wherein said estimation techniques comprises 10 or more estimationtechniques.
 13. The energy control system as recited in claim 8, whereinsaid event is signaled by a timer set to a periodic interval.
 14. Theenergy control system as recited in claim 13, wherein said periodicinterval comprises 24 hours.
 15. A method for controlling energyconsumption, the method comprising: employing interval based energyconsumption streams corresponding to a facility to develop and maintainweather-normalized baseline energy consumption data for the facility,wherein the weather-normalized baseline energy consumption data isderived from training data for the facility; providing tagged energyconsumption data sets that are each associated with a corresponding oneof the interval based energy consumption streams, each of the taggedenergy consumption data sets comprising groups of contiguous intervalvalues tagged as having been validated, wherein said groups correspondto correct data; reading the post VEE readings data stores uponinitiation of an event; for the each of the tagged energy consumptiondata sets, creating anomalies having different durations using only thegroups of contiguous interval values; generating estimates for theanomalies by employing estimation techniques for each of the differentdurations; for the each of the different durations, selecting acorresponding one of the estimation techniques for subsequent employmentwhen performing VEE of subsequent energy consumption data associatedwith the each of the different durations for the corresponding one ofthe interval based energy consumption streams; and receiving theweather-normalized baseline energy consumption data and post VEEreadings data, and developing an energy consumption model based on theweather-normalized baseline energy consumption data and the post VEEreadings data, and controlling overall energy consumption within thefacility based on the energy consumption model by scheduling run timesof one or more building elements.
 16. The method as recited in claim 15,wherein the corresponding one of the estimation techniques is selectedbecause its corresponding estimates are more accurate than estimatesgenerated by remaining ones of the plurality of estimation techniques.17. The method as recited in claim 15, wherein estimation rules for allof the interval based energy consumption streams in parallel with realtime processing of the one or more interval based energy consumptionstreams within a network operations center, and wherein the intervalbased energy consumption streams are processed serially.
 18. The methodas recited in claim 17, wherein VEE for the interval based energyconsumption streams must be completed within five minutes.
 19. Themethod as recited in claim 15, wherein the estimation techniquescomprise 10 or more estimation techniques.
 20. The method as recited inclaim 15, wherein the event is signaled by a timer set to a periodicinterval.